The Leadership Algorithm

What It Takes to Lead in the Age of Data, AI, and Intelligent Systems
1. Auflage 2026
ISBN: 979-8-8688-2896-6
Verlag: APRESS L.P.

Buch, Englisch, 206 Seiten, Format (B × H): 155 mm x 235 mm

In a world flooded with data but starved for insight, this book gives data leaders the blueprint they’ve been missing. Written for CDOs, CDAOs, and rising data executives, it demystifies why so many organizations fail to realize value from their data investments and offers a practical, actionable path to turn data and AI into engines of real business transformation.

Navigating the paradox of modern data leadership, the book exposes the structural, cultural, and strategic blind spots that hold companies back. It reveals how even the most data-rich enterprises can fall victim to the “data illusion” and how leaders can break the cycle.

More than theory, this is a hands-on guide that shows you how to design high-impact data organizations, build a culture that embraces insight-driven decisions, and align data strategy with long-term business goals. From data products to agentic AI, you’ll get the clarity, frameworks, and language needed to influence stakeholders and lead confidently in an era of rapid technological change.

The book also takes an unflinching look at ethical AI, offering lessons from missteps and practical approaches to rebuild trust through transparency and responsible governance. Rather than treating governance as a barrier, you’ll learn to leverage it as a catalyst for innovation and scale.

Whether you’re steering an enterprise data function or stepping into a leadership role for the first time, this book equips you with the strategic mindset and the organizational roadmap to build the intelligent organizations of tomorrow.

What You Will Learn

How to prepare your organization for emerging technologies like agentic AI

How to design high-impact teams and structures

How to build a data-driven culture

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Shivam, Shrey

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Wirtschaftswissenschaften Betriebswirtschaft Management Strategisches Management

Inhaltsverzeichnis

Chapter 1: The Data Dreamer and the AI Doomsayers.- Chapter 2: The Conveyor Belt Problem.- Chapter 3: The Architect’s Blueprint.- Chapter 4: The Culture Conundrum.- Chapter 5: The Tale of Two Strategies.- Chapter 6: The Insight Factory.- Chapter 7: Breaking the Monetization Maze.- Chapter 8: From Data Governance to Data Enablement.- Chapter 9: Data-Centric AI: A Paradigm Shift.- Chapter 10: Data Ethics and the Phoenix Effect.- Chapter 11: The Virtuous Cycle.- Chapter 12: The Future of Data and AI Leadership.

Über Autor(innen)

Shrey Shivam is a Senior Director at Fiserv, based in Ireland, and he is currently head the data, analytics and AI function for EMEA. In his current role, he drives data strategy and leads the modernization of the data landscape to enable Fiserv to harness advanced analytics and AI capabilities to further disrupt the payment processing ecosystem.

As a data product and data technology leader, Shrey works with the executive leadership of Fiserv to promote the organization to build direct and indirect monetization capabilities using data. With relevant exposure of various domains such as digital media, e-commerce, B2C startups, investment banking, insurance, fintech and fortune 50 companies, he has acquired a wealth of a high-level expertise for the modern technological landscape in leading solutions around Big Data, Machine Learning, Enterprise BI, content discovery based information retrieval systems, Recommendation Engines, Graph Databases and Natural language processing based platforms such as context based chatbots, semantic web and structured search. In his previous role he was responsible for leading the architecture, design, development and delivery of various statistical and predictive modeling use cases focusing on customer experience, fraud, underwriting, leads management and NLP based cognitive data discovery by functionally managing a specialized team of data scientists, ML & Data engineers across US, Ireland and India.